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University Physics I · topic UPI-15.08

Viscosity and turbulence introduction

Connect the ideas, make your working visible, and test the result. Use the hints when you need a starting point; open the full solution after your attempt.

Further practice

Work through a fresh case before tackling the extension.

Further practice · 01

Viscosity & Turbulence: predict a finite change

Viscosity is a fluid's resistance to being sheared, and the Reynolds number compares inertial with viscous effects to predict whether a flow is laminar or turbulent. Use the supplied topic model Y_out = v*d*1000/nu, where Y_out is REYNOLDS NUMBER vd/ν (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. nu is Kinematic viscosity ν, measured in ×10⁻⁶ m² s⁻¹. Keep v (Mean flow speed) = 0.1 m s⁻¹; d (Pipe bore d) = 20 mm fixed. For this exercise use only 2.57187 ×10⁻⁶ m² s⁻¹ ≤ nu ≤ 17.9281 ×10⁻⁶ m² s⁻¹ on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. Calculate Y_out at nu = 5.95025 ×10⁻⁶ m² s⁻¹ and 8.10012 ×10⁻⁶ m² s⁻¹. Find the signed output change ΔY_out and the average sensitivity ΔY_out/Δnu across this interval. State whether the response increases or decreases; distinguish this interval average from a derivative at one point.

Explore the question data

REYNOLDS NUMBER vd/ν against nu

Predictions from the supplied Viscosity & Turbulence model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.

Vertical axis (y): REYNOLDS NUMBER vd/ν

REYNOLDS NUMBER vd/ν against nuPredictions from the supplied Viscosity & Turbulence model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law. Horizontal axis: nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹. Vertical axis: REYNOLDS NUMBER vd/ν. Exact values are available in the data table below.84.91264.8444.6624.4804.31.9586.10410.2514.418.54xy

Horizontal axis (x): nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹: 2.572REYNOLDS NUMBER vd/ν: 777.6
Read the exact data as a table
REYNOLDS NUMBER vd/ν against nu — supplied values
nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹REYNOLDS NUMBER vd/ν
2.571875777.6427703523694
6.410937499999999311.96685352181333
10.249999999999998195.12195121951223
14.089062499999999141.95408672507486
17.928124999999998111.55656266341295

These are the supplied values, not a worked answer. No fitted trend is drawn.

Show a hint

Substitute each value of nu separately while keeping every other input fixed, then subtract in the same order.

Reveal the worked solution
  1. At nu = 5.95025 ×10⁻⁶ m² s⁻¹, substitute into Y_out = v*d*1000/nu: Y₀ = 336.12.
  2. At nu = 8.10012 ×10⁻⁶ m² s⁻¹, the same fixed-parameter model gives Y₁ = 246.91.
  3. Signed change: ΔY_out = Y₁ − Y₀ = -89.2106; the input change is Δnu = 2.14987 ×10⁻⁶ m² s⁻¹.
  4. Average sensitivity is ΔY_out/Δnu = -41.4957 1 per ×10⁻⁶ m² s⁻¹. Its negative sign means that this response decreases over the specified interval.
  5. This is a secant slope between two settings. It equals a point derivative only for a locally linear relation or in a justified small-interval limit.

Answer Y₀ = 336.12, Y₁ = 246.91; ΔY_out = -89.2106; average sensitivity = -41.4957 1 per ×10⁻⁶ m² s⁻¹.

Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.

Extra challenging questions

Work through the linked stages and explain what each result means.

Extra challenging · 01

Viscosity & Turbulence: infer an input with uncertainty

Viscosity is a fluid's resistance to being sheared, and the Reynolds number compares inertial with viscous effects to predict whether a flow is laminar or turbulent. Use the supplied topic model Y_out = v*d*1000/nu, where Y_out is REYNOLDS NUMBER vd/ν (dimensionless). The output symbol Y_out is distinct from all input symbols. ^ denotes a power; π = 3.141592653589793. nu is Kinematic viscosity ν, measured in ×10⁻⁶ m² s⁻¹. Keep v (Mean flow speed) = 0.1 m s⁻¹; d (Pipe bore d) = 20 mm fixed. For this exercise use only 2.57187 ×10⁻⁶ m² s⁻¹ ≤ nu ≤ 17.9281 ×10⁻⁶ m² s⁻¹ on this continuous branch. A grid of test settings is an input restriction for this exercise, not by itself a claim of physical quantisation. This calculation retains the topic model's stated assumptions; it does not extend them to a different physical setup. A reading gives Y_out = 167.51559, with a bounded reading uncertainty ±8.3939. Treat the model and all fixed inputs as exact for this exercise; the bound is not a standard deviation. First estimate the unknown nu by straight-line interpolation between the two endpoint predictions. Then solve the actual model on the stated branch to an absolute input tolerance of 0.00153562 ×10⁻⁶ m² s⁻¹, using a numerical bracket if required. Find the full input interval consistent with the bounded reading uncertainty and compare it with a symmetric input-error assumption. Do not assume that the supplied calibration points prove monotonicity outside this branch.

Explore the question data

REYNOLDS NUMBER vd/ν against nu

Predictions from the supplied Viscosity & Turbulence model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law.

Vertical axis (y): REYNOLDS NUMBER vd/ν

REYNOLDS NUMBER vd/ν against nuPredictions from the supplied Viscosity & Turbulence model, with all other inputs fixed as stated in the question. 5 calibration points support a trend comparison; they are not measured data or a straight-line law. Horizontal axis: nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹. Vertical axis: REYNOLDS NUMBER vd/ν. Exact values are available in the data table below.84.91264.8444.6624.4804.31.9586.10410.2514.418.54xy

Horizontal axis (x): nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹

Axes show the supplied calibration interval; the origin may lie outside the plot.

Reading 1nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹: 2.572REYNOLDS NUMBER vd/ν: 777.6
Read the exact data as a table
REYNOLDS NUMBER vd/ν against nu — supplied values
nu: Kinematic viscosity ν / ×10⁻⁶ m² s⁻¹REYNOLDS NUMBER vd/ν
2.571875777.6427703523694
6.410937499999999311.96685352181333
10.249999999999998195.12195121951223
14.089062499999999141.95408672507486
17.928124999999998111.55656266341295

These are the supplied values, not a worked answer. No fitted trend is drawn.

Show a hint

The model's endpoint predictions bracket the reading. Solve Y_out(nu) = Y_measured and then solve again at Y_measured − u and Y_measured + u; a nonlinear inverse can give unequal input deviations.

Reveal the worked solution
  1. Endpoint predictions are Y_out(2.57187) = 777.643 and Y_out(17.9281) = 111.557. The target lies between them. On the selected branch the response is strictly decreasing.
  2. Endpoint interpolation gives nu_line = 2.57187 + (167.516 − 777.643)(17.9281 − 2.57187)/(111.557 − 777.643) = 16.638 ×10⁻⁶ m² s⁻¹. This estimates the inverse of the endpoint chord, rather than assuming that chord is the physical model.
  3. Use the actual equation, not the chord, and bracket its root. Evaluating the midpoint and retaining the sign-changing half-interval gives nu = 11.9392 ×10⁻⁶ m² s⁻¹; substituting back gives Y_out = 167.51559.
  4. The interpolation error in the input is |nu_line − nu| = 4.69883 ×10⁻⁶ m² s⁻¹. This is larger than the requested numerical tolerance.
  5. Invert both limits Y_out = 159.12169 and 175.90949. Sort the roots even if the response decreases: 11.3695 ×10⁻⁶ m² s⁻¹ ≤ nu ≤ 12.569 ×10⁻⁶ m² s⁻¹.
  6. The lower and upper input deviations are 0.569704 ×10⁻⁶ m² s⁻¹ and 0.629809 ×10⁻⁶ m² s⁻¹. They differ because the inverse response is nonlinear; a symmetric error bar would only be a local approximation. This interval includes reading uncertainty only, not model error or uncertainty in fixed inputs.

Answer nu ≈ 11.9392 ×10⁻⁶ m² s⁻¹; compatible interval [11.3695, 12.569] ×10⁻⁶ m² s⁻¹. Endpoint interpolation gives 16.638 ×10⁻⁶ m² s⁻¹.

Compare the assumptions, method and units with your attempt. Explain any correction before trying another question.